Working as a Data Analyst

Job Profile of a Data Analyst

A Typical Job Profile of a Data Analyst:

A data analyst plays a crucial role in transforming data into actionable insights that help organizations make better-informed decisions. The job profile can vary depending on the industry and the type of organization, but there are several common skills and knowledge areas that most data analysts possess. Below is an overview of the key requirements and skills.

1. Education and Background

• Someone with a relevant bachelor's or academic degree, preferably in data science, statistics, computer science, mathematics, or a related technical discipline.
• Experience with the application of data analysis methods, both in theory and in practice, is essential for obtaining effective insights.

2. Storytelling with Data

• Strong in storytelling based on numbers: The ability to interpret complex datasets and convey them in an understandable way to both technical and non-technical stakeholders is essential.
• This means translating raw numbers into meaningful insights and presenting findings in a visually appealing and understandable format, such as through dashboards or reports.

3. Advanced Microsoft Excel Skills

• An expert in Microsoft Excel: Someone who masters advanced Excel functions such as VLOOKUP(), MATCH(), SUMIF(), INDEX/MATCH, and Power Query to effectively transform, filter, and analyze data.
• Proficiency in using pivot tables and charts to analyze and visually present datasets.

4. Data Visualization and Reporting

• Experience with Microsoft Power BI, Tableau, Qlik, or other data visualization tools to build interactive dashboards that provide insights into business data.
• The ability to generate reports that are not only informative but also visually appealing and easy to understand for different audiences.

5. Knowledge of Statistics and Advanced Analytical Methods

• Strong knowledge of statistics: This ranges from basic techniques such as regression analysis to more complex methods like machine learning models and time series analysis.
• Experience using statistical software such as R, Python (with libraries like Pandas, NumPy, Matplotlib), and STATA to perform in-depth analyses.

6. Skills in Data Queries and Databases

• The ability to write data queries in SQL and other query languages to retrieve data from relational databases and data warehouses.
• Experience with data providers such as Bloomberg, Refinitiv, and other specialized systems is a plus, especially in sectors like finance or healthcare.

7. Knowledge of Machine Learning and Cloud Tools

• Basic knowledge of machine learning algorithms and experience applying these techniques to large datasets to build predictive models.
• Experience with tools like Azure Machine Learning, Google AI, or Amazon SageMaker to develop, train, and deploy machine learning models in the cloud.

8. Basic Knowledge of Data Pipeline Architecture

• Basic knowledge of data pipelines, ETL processes (Extract, Transform, Load), and data integration is important to understand how data flows through various systems and can be analyzed.
• Experience with cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform can be a significant advantage in modern data environments.

9. Communication and Collaboration Skills

• Strong communication skills to clearly convey insights and analyses to various stakeholders, including managers, department heads, and technical teams.
• Experience working with multidisciplinary teams, where data analysts often serve as a bridge between technical and non-technical departments.

Which tools does a Data Analyst use

SQL

SQL (Structured Query Language) remains the primary language for querying and managing relational databases. In 2025, SQL remains indispensable for data analysts, with an even greater focus on optimization, cloud integration (such as BigQuery and Snowflake), and integration with modern data stack tools like dbt. SQL also plays a critical role in supporting AI-driven analytical tools and automating data flows.

Python and R

Python is still the most popular language in data science, with powerful libraries like pandas, scikit-learn, and PyTorch. R remains popular in academic and statistical environments, particularly for complex data analysis and visualizations. In 2025, the emphasis is on AI integrations, data flow automation, and advanced data visualization through notebooks and frameworks.

Excel

Excel remains a crucial tool for many organizations, especially for ad-hoc analyses and dashboards. Thanks to the integration of Python in Excel (Power BI integration) within Microsoft 365, the gap between traditional spreadsheets and advanced data analysis tools has never been smaller. Excel is becoming more powerful with the addition of machine learning and AI functionalities for data analysis and visualization.

Tableau and Power BI

Power BI and Tableau remain the primary tools for data visualization. Power BI is gaining ground in business environments due to its integration with Microsoft Teams and Excel, while Tableau is known for its visual strength and flexibility. In 2025, the emphasis is on AI-driven visualizations, natural language queries, and integration with cloud-based data warehouses for real-time insights.

Big Data Platforms

Platforms like Hadoop have become less dominant, but Apache Spark remains essential for large-scale data analysis. Kafka is widely used for real-time data streaming, and in 2025, these tools are increasingly being combined with cloud-based data lakehouses and integrations with AI-driven applications for faster insights.

SAS and SPSS

SAS and SPSS are still used for advanced statistical analyses, particularly in sectors like healthcare, government, and academic research. However, they are losing ground to open-source alternatives and cloud-native solutions that are more flexible and scalable. They remain important in regulated industries where compliance and reliability are essential.

Google Analytics

Since the switch to Google Analytics 4 (GA4), the focus is on event-based tracking and machine learning-driven insights. GA4 requires new skills, such as using BigQuery export and custom analysis. In 2025, GA4 is indispensable for professionals working with web data, digital marketing, and customer journey analysis, thanks to AI integration and improved data tracking capabilities.

Jupyter Notebook

Jupyter Notebook remains the standard for interactive data analysis, education, and prototyping. In 2025, there will be broader adoption of variants like JupyterLab and integrations with cloud platforms such as Google Colab, Azure Notebooks, and VS Code. Notebooks with built-in AI assistants for code generation and analysis have become increasingly common, making it easier to analyze and develop complex models.

GitHub

GitHub has become the platform for collaboration, version control, and documentation within data science and analytics. With new features like GitHub Copilot and GitHub Actions, data analysts and engineers can benefit from AI support and automations that significantly speed up workflows and simplify version control.

dbt (data build tool)

dbt is a powerful tool that allows data analysts and engineers to use SQL to transform and model data pipelines. It is deeply integrated into the modern data stack and makes version control, testing, and documentation of data models efficient and transparent. In 2025, we see increasing adoption of dbt in combination with cloud-native data warehouses.

Apache Airflow

Apache Airflow is a popular workflow manager for scheduling, monitoring, and managing data pipelines. It is the standard choice in many data engineering teams for orchestrating complex ETL processes. In 2025, more integrations with cloud-based data platforms and AI tools are expected, making Airflow increasingly relevant for modern data infrastructures.

Looker

Looker (part of Google Cloud) is an advanced business intelligence tool that uses LookML to centrally manage and make data models easily accessible to data consumers. In 2025, Looker is gaining popularity, especially in data-driven organizations working with Google BigQuery and other cloud-native solutions, thanks to the power of real-time data and analytics.

Snowflake

Snowflake is a cloud-native data platform that offers superior scalability for both storage and compute. With its separate architecture for storage and compute, Snowflake is ideal for data warehousing, business intelligence, and advanced analytics. In 2025, it remains the preferred choice for companies needing cloud-based data solutions, with strong integrations with tools like dbt, Airflow, and Python.

Google BigQuery

BigQuery is Google Cloud's serverless data warehouse, providing powerful analytics capabilities for massive datasets. In 2025, BigQuery remains a fixture in many marketing, e-commerce, and data science environments, thanks to its speed, simplicity, and integration with tools like Looker Studio, GA4, and cloud-based AI applications.

Notebooks with AI Integration

Tools like Hex, Deepnote, and Observable combine the power of notebooks with real-time collaboration and AI assistance. These tools are becoming increasingly popular as an alternative or complement to Jupyter for modern data teams wanting to integrate AI functionalities into their workflows and analyses.

AI Tools for Data Analysis

In 2025, AI assistants like ChatGPT, Claude, and Copilot are integrated into analytics platforms and notebooks, helping to generate code, explain results, and accelerate model development and documentation. Tools like MonkeyLearn and DataRobot also make AI applications more accessible for data analysis and process automation.

MLOps Platforms

Tools like MLflow, Weights & Biases, and SageMaker are essential in 2025 for training, managing, and deploying machine learning models in production environments. MLOps platforms are crucial for teams looking to develop and deploy scalable, reliable, and repeatable AI applications.

Data Quality and Observability

Data quality and monitoring remain a priority for many companies. Tools like Great Expectations, Monte Carlo, Datafold, and OpenLineage offer insights into data flows, detect data leaks, and monitor anomalies. These tools are more important than ever in 2025 for data teams aiming to ensure reliable and high-quality data.

A Day in the Life of a Data Analyst

08:00 - 10:00: The Morning Start of a Data Analyst

Imagine this: it's 8:00 AM, and the coffee is ready. The Data Analyst opens his/her laptop and dives straight into the first dataset of the day. The morning is all about digging into the numbers: using tools like Python, R, and SQL to create complex queries to uncover patterns and trends in the data. Sometimes, the data isn't in the right format or has gaps. Then the process of cleaning and filtering begins, which often takes up the first few hours of the workday. It’s a time-consuming but necessary process because only clean and reliable data can provide real value to the business.

12:00 - 14:00: From Data to Insights

Between 12:00 PM and 2:00 PM, it's time to turn the data into something more understandable. The Data Analyst starts visualizing their findings using powerful tools like Tableau or Power BI. Clear graphs, tables, and dashboards are created to make trends and relationships visible to the stakeholders. Now, the art is not just to present the numbers but also to ensure the message gets across: what do these data points mean for the business? How can these insights contribute to strategic decision-making? The afternoon is often spent fine-tuning these visualizations and reports so everything is ready for the presentation.

15:00 - 17:00: Stakeholder Meetings and Collaboration

Starting from 3:00 PM, there are regular meetings and discussions with other departments and stakeholders. These conversations are crucial to ensure that the analyses align with business goals and needs. It's important for the Data Analyst to not only have a strong understanding of data but also to fully grasp where the company is headed. Whether it’s improving customer satisfaction, optimizing internal processes, or identifying new market opportunities, the Data Analyst must be able to communicate their insights clearly and work with others to find solutions. This is the moment when data truly comes to life, bridging the gap between numbers and business strategy.

17:00 - 18:00: Professional Development and Growth

Around 5:00 PM, as the workday slowly winds down, it’s time for the Data Analyst to focus on the future. The world of data analysis is rapidly changing, and staying up-to-date with new technologies and methods is crucial. Many Data Analysts use the evening to continue learning by taking online courses, attending webinars, or reading industry literature. Machine learning, AI, and new analytical methods are just a few of the areas the Data Analyst continuously explores. This ongoing learning process ensures they are always prepared for the challenges of tomorrow.

The Essential Skills of a Data Analyst

What makes a Data Analyst successful? The ability to work with data is obviously important, but it goes beyond that. A Data Analyst must also possess excellent communication skills so that complex technical analyses can be presented understandably to non-technical stakeholders. Additionally, the ability to recognize patterns and think strategically is crucial. The combination of technical expertise and business insight makes the Data Analyst a valuable link in the company’s success.

What does a data analyst earn

The salary of a Data Analyst can vary significantly depending on factors such as experience, location, the industry in which they work, and the size of the company. Data Analysts play a crucial role in analyzing data to generate valuable insights that help businesses make strategic decisions. Below is an overview of salary expectations based on different experience levels.

Entry-level (Junior Data Analyst)

A Junior Data Analyst, who is just starting in the role and may have completed a degree in statistics, computer science, or a related field, can expect a salary of €2,800 to €3,800 per month. This is the entry-level for someone who typically works under supervision and is responsible for simple data analysis tasks such as collecting, cleaning, and visualizing data. Junior Data Analysts may work with tools such as Excel, SQL, and basic data analysis tools.

Mid-level (Data Analyst)

A Data Analyst with a few years of experience (usually between 2 and 5 years) can earn between €3,800 and €5,000 per month. At this level, the analyst generally has more experience with performing more complex data analyses, working with databases, and using advanced tools such as SQL, Python, R, or business intelligence software. Data Analysts at this level are capable of conducting independent data analyses, generating reports, and presenting key insights to various departments within the company.

Senior level (Senior Data Analyst)

A Senior Data Analyst, with more than 5 years of experience, can expect a salary between €5,000 and €6,500 per month. Senior Data Analysts have extensive experience in performing advanced analyses, developing predictive models, and advising management on strategic decisions based on data insights. They often play a leading role in the data analysis process and may be responsible for training and mentoring junior team members.

Lead Data Analyst

The role of Lead Data Analyst is common in larger organizations and comes with managerial responsibilities. These analysts can earn between €6,500 and €8,000 per month. They lead a team of data analysts, provide strategic direction for data initiatives within the company, and work closely with other departments such as marketing, finance, or IT to implement data-driven solutions. Lead Data Analysts are experts in data analysis and often have in-depth knowledge of complex analytical tools and methods.

Location and Industry

Location has a significant impact on the salary of a Data Analyst. In cities such as Amsterdam, Rotterdam, and Utrecht, salaries are generally higher due to the presence of many technology companies, financial institutions, and international organizations. The industry also plays an important role; Data Analysts working in sectors such as finance, technology, or healthcare can often expect higher salaries than those in other industries. Companies working with large amounts of data or advanced technologies also tend to offer higher salaries.

Education and Skills

The education and skills of a Data Analyst have a significant impact on salary. Data Analysts with a master's degree in a related field such as mathematics, statistics, computer science, or econometrics often have higher salary expectations. In addition, practical skills such as experience with tools like SQL, Python, R, and business intelligence software like Tableau or Power BI can further increase salary. Skills in data visualization, statistical modeling, and data mining can also contribute to a higher salary.

Career Path and Growth Opportunities as a Data Analyst

As a data analyst gains experience, various growth opportunities become available. Here are some of the possible career paths a data analyst can pursue:

Senior Data Analyst

With sufficient experience, a data analyst can advance to a role as a senior data analyst. This will focus on mentoring junior team members, taking responsibility for more complex projects, and improving overall data governance within the organization.

Data Scientist

A data analyst can further specialize and grow into a data scientist. This requires in-depth knowledge of advanced statistical models, machine learning, and artificial intelligence. Additionally, experience with big data analysis methods and programming languages such as Python or R is often necessary. A master's or PhD degree in a relevant field is often a requirement to fulfill this role.

Business Intelligence Analyst

As a Business Intelligence Analyst, the focus is on analyzing data to generate actionable insights that can guide business decisions. This includes developing dashboards, reports, and KPIs (Key Performance Indicators), and working with data warehousing solutions and visualization tools such as Power BI or Tableau.

Data Engineer

A data analyst can focus on the technical aspects of data analysis, such as designing, building, and maintaining data sources and pipelines. Knowledge of big data storage, ETL processes, and cloud computing platforms such as AWS, Azure, and Google Cloud are essential in this role. Data engineers play a crucial role in creating a robust and scalable infrastructure for data analysis.

Leadership Roles

With sufficient experience and proven leadership skills, a data analyst can move into management positions such as team leader, department head, or even Chief Data Officer (CDO) within an organization. These roles require not only technical expertise but also strong communication and strategic skills to effectively translate data insights into business strategies.

Continuous Professional Development

It is important for a data analyst to continuously stay updated with the latest techniques, tools, and trends in the industry. Taking courses, certifications, workshops, and attending conferences are some ways to acquire and keep up with relevant skills. With the rapid advancements in AI, machine learning, and data analysis technologies, it is essential to continually invest in professional development.

Networking and Industry Organizations

There are several industry organizations that focus on data analysis and related fields. Some examples of such organizations include:

INFORMS (Institute for Operations Research and the Management Sciences)

INFORMS is a global, interdisciplinary association that focuses on advancing research and the application of operations research, management sciences, and data analysis. The organization supports professionals by providing networking opportunities, knowledge sharing, and developing standards for the practice of data analysis and decision-making.

IIBA (International Institute of Business Analysis)

IIBA is a non-profit organization that promotes the development of business analysis worldwide. The organization plays a crucial role in strengthening the role of business analysts and data analysts through certification, training programs, and providing guidelines that help improve business operations and processes within organizations.

DAMA (Data Management Association)

DAMA is a global organization that focuses on promoting best practices and knowledge sharing in the field of data management. With an emphasis on the value of data as a strategic business asset, DAMA also offers certification programs and professional development for data managers, with a focus on data governance, data quality, and security.

TDWI (The Data Warehousing Institute)

TDWI is a professional organization that focuses on improving knowledge and skills in the areas of data warehousing, business intelligence (BI), and data analysis. The organization offers training, events, and a community where professionals in data analysis and BI can enhance their knowledge and exchange best practices.

Impact and societal relevance

What does a Data Analyst do exactly?
A Data Analyst is someone who investigates large amounts of data and transforms it into valuable insights. In a world where data is the key to many business and societal decisions, the Data Analyst plays an undeniable role. By analyzing data, they discover patterns that can help companies make better choices, improve processes, and even develop new ideas. The work of a Data Analyst has a big impact, and more and more organizations are realizing how important it is to use data to achieve their goals.

Why is a Data Analyst important for companies?
Data Analysts are crucial for companies that want to make smart decisions. They help organizations by discovering trends and relationships in data that might otherwise go unnoticed. This allows companies to respond more quickly to changes, better understand customer needs, and improve their products or services. The result? Higher efficiency, lower costs, and new opportunities that contribute to competitive advantage. By making good use of data, companies can achieve more success in an ever-changing market.

The impact on society
The influence of Data Analysts goes beyond just businesses. They play a significant role in solving important societal issues. Whether it's healthcare, the environment, urban planning, or finance, data is increasingly being used to address complex problems. In healthcare, for example, Data Analysts help analyze patient data to deliver better care and predict diseases more accurately. In the fight against climate change, they can help develop sustainable solutions. The work of a Data Analyst, therefore, has a direct impact on the well-being of society.

Transparency and trust
In the modern world, organizations are expected to be transparent and accountable. This also applies to how they make decisions. This is where the Data Analyst comes into play. By presenting data in a clear and responsible way, they can help organizations justify their choices. This not only builds trust with customers and partners but also shows that the organization handles information ethically and carefully. In a time when transparency is an important value, the Data Analyst plays a key role.

The future of the Data Analyst
The future looks bright for Data Analysts. The amount of data continues to grow, and with new technologies like artificial intelligence (AI) and machine learning, Data Analysts will soon be able to conduct even deeper and more detailed analyses. This opens new doors for companies to further improve their performance. The societal role of the Data Analyst will also become increasingly important. With data, important issues such as sustainability, healthcare, and social justice can be tackled more effectively. The role of the Data Analyst will therefore continue to grow, both in the business world and in broader society.

Case Study: The Impact of a Data Analyst

Background

HealthInnovate is an innovative company focused on health technology, using the latest tools and technologies to improve the health and well-being of their customers. Over the years, the company has gathered increasing amounts of data from various sources, such as customer feedback, product usage, and market research. The challenge, however, was to effectively analyze and interpret this data, given the scale and complexity of the information generated on a daily basis.

The Challenge

Despite the abundance of data, the HealthInnovate team struggled to convert this raw data into actionable insights that could strengthen the business strategy and product development. The lack of the right analytical tools and expertise meant that valuable information was often overlooked. This led to suboptimal strategic choices and limited the ability to increase customer satisfaction and improve products. The company urgently needed a solution that would help them make better use of data and integrate data-driven decision-making into their daily processes.

Action by the Data Analyst

Johan, an experienced Data Analyst, was appointed to tackle this challenge. He introduced advanced data analysis methods and implemented the latest tools to structure and analyze the vast amount of collected data. Johan worked closely with various departments, including marketing, product development, and customer service, to understand the specific needs and pain points within the company. Through this collaboration, he gained in-depth insights into the business processes and the impact of data on daily decision-making.

With these insights, Johan developed customized dashboards and interactive reports that translated complex data into understandable, visual insights. These reports were not only easy to comprehend but could also be quickly shared with all involved teams, resulting in a more collaborative and informative work environment. Thanks to Johan's efforts, management and teams were able to make informed, data-driven decisions that significantly improved customer satisfaction and product quality.

Result

The impact of Johan's work was immediately noticeable. HealthInnovate was able to better align its products with the specific desires and needs of customers, resulting in a significant increase in customer satisfaction. Moreover, the integration of data-driven decision-making led to more efficient product development cycles and an increased speed of innovations. Johan's analyses enabled the company to quickly identify relevant market trends, leading to new business opportunities and an improved competitive position in the market.

This transformation into a data-driven company not only led to product improvements but also to a cultural shift within HealthInnovate. Data was no longer seen as a peripheral element but as a strategic tool essential to the company's success. Since then, HealthInnovate has optimized its business processes and significantly increased its growth potential, with data analysis driving continuous innovation and customer-centric strategies.

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